Rapid increases in the cost of healthcare are creating a growing demand for ways to reduce costs while minimizing the impact on healthcare quality and maintaining a high standard of care. HMOs, higher co-pays, tiered co-pays, formulary control, step therapy, disease management, predictive modeling, high-deductible plans, wellness plans, negotiated rates, referral requirements, centers of excellence, pre-authorization, offshore surgery options, wellness programs, and various educational and preventive programs are all initiatives driven by this goal.
The search for approaches that save money by improving care in cost-effective ways has led to screening and prevention measures such as blood pressure control, lipid-lowering therapy, weight reduction, aspirin, and cancer screening. These approaches are of particular interest because they offer the promise of improving patient health while reducing costs by averting more expensive outcomes. Some preventive interventions, such as prescribing beta-blockers after a heart attack, have been shown to be cost-effective on a short-term basis. Other preventive interventions are not as clear, or because they take longer to achieve cost-effectiveness, have been more difficult to assess.
Researchers have investigated the cost-effectiveness of various preventive interventions. Such work focuses on the cost-effectiveness of applying medical guidelines that specify the populations to whom the interventions should be applied. Example applied medical guidelines include simple rules incorporating a few factors that a doctor might be able to remember.
Medical risk calculators assess only the risk of a health outcome. Risk calculators are primarily used for selecting populations for treatment. Furthermore, risk calculators are generally derived from statistical analysis of a longitudinal data set and have a number of associated limitations. For example, in a purely statistical model, the size of the dataset limits the number of variables that can be fitted reasonably. Statistical analyses cannot model new interventions for which there is no data. As well, statistical analyses cannot model combinations of interventions which haven't been tested and for which there is no data. As well, statistical analyses cannot predict changes in a population for which there is no data. As well, statistical models cannot distinguish causal differences from behavioral associations; for example a statistical model may conclude that going to the doctor makes people sick, or getting chemotherapy causes you to die of cancer. Simulation models can be constructed using evidence from many different randomized controlled trials in which the true effect of each intervention or treatment is not confounded by behavioral biases.
Some approaches for identifying members of a payer who are at risk of a medical event or qualify for an intervention include analysis of claims data, analysis of health risk assessment (HRA) data, and predictive modeling. Some approaches for managing interventions in a cost-effective manner include disease management, adherence to guidelines, and wellness programs. Identification based on claims data generally is performed only after disease diagnosis and thus may have a limited utility, e.g., an intervention opportunity may be missed. Guidelines may be “one size fits all” instruments and the interventions specified in the guidelines may not be optimally targeted.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.